Bilde av Prastyabudi, Wahyu Andy
Bilde av Prastyabudi, Wahyu Andy
Institutt for industriell teknologi wahyu.a.prastyabudi@uit.no

Wahyu Andy Prastyabudi


Doctoral Research Fellow

Stillingsbeskrivelse

Research Project Title: Towards Sustainable Supply Chain in Aquaculture Industry

Summary

The aquaculture sector plays a pivotal contribution in ensuring food security and serves as a significant driver of economic growth in the Arctic region. However, it encounters multifaceted challenges spanning from supply chain complexity to environmental sustainability. While technological advancements in aquaculture production stage are rapidly evolving, comprehensive studies on the broader supply chain network structure and its interplay processes, remain relatively scarce, particularly with a focus on sustainability. This research project delves into the development of sustainable aquaculture supply chains in the context of Industry 5.0. It involves reevaluating supply chain network configurations, performance measurement, and exploring implications for the aquaculture industry. The project unfolds in four distinct stages: 1) identifying the determinant factors of supplier selection process, 2) investigating the potential use of digital supply chain twins in optimizing supply chain efficiency while considering sustainability aspects, 3) establishing a reconfigurable supply chain network and conducting simulations to assess the system resilience, 4) model optimization and performance measurement to support decision-making. It is expected that this research will endeavour not only to enrich the empirical academic knowledge but also to offer a rigorous implication towards strategic, operational, and performance insights to the aquaculture industry. Thereby, it would enhance the competitiveness of this sector through sustainability-oriented practices.



Forskningsinteresser

Research interests include:

  • Data-driven analytics in logistics management
  • Sustainable logistics / closed-loop supply chain
  • Reconfigurable supply chain
  • Combination of both optimization method and simulation method
  • Data mining and machine learning
  • Digital supply chain twin